2 citations · 2 across the 2 of their papers we have counts for
5 papers
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI
Aniket Pramanik, Xiaodong Wu, Mathews Jacob
The volume estimation of brain regions from MRI data is a key problem in many clinical applications, where the acquisition of data at high spatial resolution is desirable. While pa…
Reconstruction and Segmentation of Parallel MR Data using Image Domain DEEP-SLR
Aniket Pramanik, Mathews Jacob
The main focus of this work is a novel framework for the joint reconstruction and segmentation of parallel MRI (PMRI) brain data. We introduce an image domain deep network for cali…
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstr…
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (S…
Off-the-grid model based deep learning (O-MODL)
Aniket Pramanik, Hemant Kumar Aggarwal, Mathews Jacob
We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies i…